Bayesian Hierarchical Grouping: perceptual grouping as mixture estimation

Bayesian hierarchical grouping: Perceptual grouping as mixture estimation.

We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian hierarchical grouping (BHG). In BHG, we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters...

Perceptual grouping as Bayesian mixture estimation

Perceptual grouping is the process by which the visual system organizes the image into distinct objects or clusters. Here we briefly describe a Bayesian approach to grouping, formulating it as an inverse probability problem in which the goal is to estimate the organization that best explains the observed set of visual elements. We pose the problem as an instance of mixture modeling, in which th...

Perceptual Grouping and Change Detection Perceptual Grouping in Change Detection Perceptual Grouping and Change Detection

Detection of an item changing its location from one instance to another is typically unaffected by changes in the shape or color of contextual items. However, we demonstrate here that such location change detection is severely impaired if the elongated axes of contextual items change orientation, even though individual locations remain constant and even though the orientation was irrelevant to ...

Target-driven Perceptual Grouping

Perceptual Grouping Using Superpixels

Perceptual grouping plays a critical role in both human and computer vision. However, with the object categorization community’s preoccupation with object detection, interest in perceptual grouping has waned. The reason for this is clear: the object-independent, mid-level shape priors that form the basis of perceptual grouping are subsumed by the object-dependent, high-level shape priors define...